0.1 In which region is the interview taking place?

0.1.1 How many households would you estimate there to be in your village?“”

0.2 Graph

0.3 Table

region numbers lover conf. limit upper conf. limit
dependent.var south_east 60.72165 45.72520 75.71810
dependent.var1 capital_central 36.29179 23.72339 48.86019
dependent.var2 north_east 44.31057 34.11060 54.51053
dependent.var3 west 59.17154 50.59274 67.75033
dependent.var4 east 50.69055 42.76680 58.61431
dependent.var5 south 47.15459 35.52909 58.78010
dependent.var6 north 44.95590 39.77987 50.13193

0.4 Statistics

two sample ttest on difference in means (two sided): p=0.058; df=450

0.4.1 Is the HH in an informal settlement?

0.5 Graph

0.6 Table

informal_settlement region numbers lover conf. limit upper conf. limit
no capital_central 0.7654511 0.5284897 1.0000000
no east 0.8604435 0.7297095 0.9911776
no north 0.9285158 0.8521533 1.0000000
no north_east 0.9996894 0.9993380 1.0000000
no south 0.8364660 0.7111739 0.9617580
no south_east 0.8616757 0.7176960 1.0000000
no west 1.0000000 1.0000000 1.0000000
yes capital_central 0.2345489 0.0000000 0.4715103
yes east 0.1395565 0.0088224 0.2702905
yes north 0.0714842 0.0000000 0.1478467
yes north_east 0.0003106 0.0000000 0.0006620
yes south 0.1635340 0.0382420 0.2888261
yes south_east 0.1383243 0.0000000 0.2823040
yes west 0.0000000 0.0000000 0.0000000

0.7 Statistics

Pearson’s X^2: Rao & Scott adjustment: p=NA; ddf=1049.2

0.7.1 Do you consider your household to be in which of the following options?:

0.8 Graph

0.9 Table

urban_rural region numbers lover conf. limit upper conf. limit
rural capital_central 0.0859723 0.0000000 0.2386351
rural east 0.6878900 0.5192686 0.8565115
rural north 0.1841642 0.0000000 0.3699684
rural north_east 0.1498344 0.0019484 0.2977204
rural south 0.3054369 0.1479624 0.4629113
rural south_east 0.8463037 0.7022782 0.9903291
rural west 0.6537766 0.5186535 0.7888997
urban capital_central 0.2998078 0.1053762 0.4942395
urban east 0.0818542 0.0000000 0.1656368
urban north 0.0278835 0.0147803 0.0409867
urban north_east 0.3783004 0.1836979 0.5729029
urban south 0.4961044 0.3281502 0.6640587
urban south_east 0.0606195 0.0000000 0.1685505
urban west 0.2670864 0.1516881 0.3824847
peri_urban capital_central 0.6142199 0.3670587 0.8613811
peri_urban east 0.2302557 0.0736241 0.3868873
peri_urban north 0.7879523 0.6019649 0.9739397
peri_urban north_east 0.4718652 0.2693940 0.6743364
peri_urban south 0.1984587 0.0792751 0.3176423
peri_urban south_east 0.0930769 0.0000000 0.2028657
peri_urban west 0.0791370 0.0000000 0.1652041

0.10 Statistics

Pearson’s X^2: Rao & Scott adjustment: p=NA; ddf=2639.1

0.10.1 Is your community densely populated?

0.11 Graph

0.12 Table

densely_populated region numbers lover conf. limit upper conf. limit
no capital_central 0.0049895 0.0004028 0.0095761
no east 0.2115835 0.0703957 0.3527714
no north 0.5785194 0.3422508 0.8147880
no north_east 0.1218779 0.0000000 0.2527405
no south 0.2238099 0.0872464 0.3603734
no south_east 0.6757398 0.4732629 0.8782166
no west 0.2855492 0.1327553 0.4383431
yes capital_central 0.9950105 0.9904239 0.9995972
yes east 0.7884165 0.6472286 0.9296043
yes north 0.4214806 0.1852120 0.6577492
yes north_east 0.8781221 0.7472595 1.0000000
yes south 0.7761901 0.6396266 0.9127536
yes south_east 0.3242602 0.1217834 0.5267371
yes west 0.7144508 0.5616569 0.8672447

0.13 Statistics

Pearson’s X^2: Rao & Scott adjustment: p=NA; ddf=1319.6

0.13.1 Do you have physical access to a market?

0.14 Graph

0.15 Table

access_to_market region numbers lover conf. limit upper conf. limit
no capital_central 0.1476270 0.0000000 0.3251322
no east 0.1703880 0.0406819 0.3000942
no north 0.1401449 0.0000000 0.3132652
no north_east 0.0709255 0.0000000 0.1741371
no south 0.1309661 0.0622704 0.1996618
no south_east 0.3163729 0.1145840 0.5181617
no west 0.2369566 0.0961317 0.3777814
yes capital_central 0.8523730 0.6748678 1.0000000
yes east 0.8296120 0.6999058 0.9593181
yes north 0.8598551 0.6867348 1.0000000
yes north_east 0.9290745 0.8258629 1.0000000
yes south 0.8690339 0.8003382 0.9377296
yes south_east 0.6836271 0.4818383 0.8854160
yes west 0.7630434 0.6222186 0.9038683

0.16 Statistics

Pearson’s X^2: Rao & Scott adjustment: p=NA; ddf=1520.5

0.16.1 Do you agree to be contacted for any follow up questions regarding this assessment?

0.17 Graph

0.18 Table

agree_to_be_contacted region numbers lover conf. limit upper conf. limit
no capital_central 0.3626020 0.1154985 0.6097054
no east 0.0329167 0.0000000 0.0874895
no north 0.9067940 0.8310268 0.9825612
no north_east 0.4477299 0.2758766 0.6195833
no south 0.4623950 0.2962485 0.6285416
no south_east 0.9403237 0.8324112 1.0000000
no west 0.2376247 0.1221032 0.3531462
yes capital_central 0.6373980 0.3902946 0.8845015
yes east 0.9670833 0.9125105 1.0000000
yes north 0.0932060 0.0174388 0.1689732
yes north_east 0.5522701 0.3804167 0.7241234
yes south 0.5376050 0.3714584 0.7037515
yes south_east 0.0596763 0.0000000 0.1675888
yes west 0.7623753 0.6468538 0.8778968

0.19 Statistics

Pearson’s X^2: Rao & Scott adjustment: p=NA; ddf=1410.4

0.19.1 Is the household head male or female?

0.20 Graph

0.21 Table

head_hh_gendre region numbers lover conf. limit upper conf. limit
male capital_central 0.9932326 0.9873176 0.9991476
male east 0.8845817 0.7626671 1.0000000
male north 0.9963517 0.9904945 1.0000000
male north_east 0.8917533 0.7619665 1.0000000
male south 0.9517157 0.9028104 1.0000000
male south_east 0.9391088 0.8306868 1.0000000
male west 0.9089502 0.8205162 0.9973841
female capital_central 0.0067674 0.0008524 0.0126824
female east 0.1154183 0.0000000 0.2373329
female north 0.0036483 0.0000000 0.0095055
female north_east 0.1082467 0.0000000 0.2380335
female south 0.0482843 0.0000000 0.0971896
female south_east 0.0608912 0.0000000 0.1693132
female west 0.0910498 0.0026159 0.1794838

0.22 Statistics

Pearson’s X^2: Rao & Scott adjustment: p=NA; ddf=960.1

0.22.1 How old is the household head?

0.23 Graph

0.24 Table

region numbers lover conf. limit upper conf. limit
dependent.var south_east 48.92053 45.51463 52.32643
dependent.var1 capital_central 43.09471 39.32370 46.86572
dependent.var2 north_east 45.07540 40.37499 49.77580
dependent.var3 west 47.28778 42.81546 51.76010
dependent.var4 east 43.78893 40.45028 47.12757
dependent.var5 south 45.79618 41.78259 49.80977
dependent.var6 north 47.04796 40.63152 53.46441

0.25 Statistics

two sample ttest on difference in means (two sided): p=0.787; df=450

0.25.1 Does the household head have a physical or mental disability that affects daily life?

0.26 Graph

0.27 Table

head_hh_disability region numbers lover conf. limit upper conf. limit
no capital_central 0.9260707 0.7886016 1.0000000
no east 0.9585870 0.8777366 1.0000000
no north 0.9879302 0.9776471 0.9982134
no north_east 0.8525609 0.6930337 1.0000000
no south 0.9810805 0.9589760 1.0000000
no south_east 0.8352101 0.6736308 0.9967895
no west 0.7324741 0.5819528 0.8829953
yes capital_central 0.0739293 0.0000000 0.2113984
yes east 0.0414130 0.0000000 0.1222634
yes north 0.0120698 0.0017866 0.0223529
yes north_east 0.1474391 0.0000000 0.3069663
yes south 0.0189195 0.0000000 0.0410240
yes south_east 0.1647899 0.0032105 0.3263692
yes west 0.2675259 0.1170047 0.4180472

0.28 Statistics

Pearson’s X^2: Rao & Scott adjustment: p=NA; ddf=1322.9

0.28.1 How many individuals are in the household?

0.29 Graph

0.30 Table

region numbers lover conf. limit upper conf. limit
dependent.var south_east 12.99550 8.588316 17.40269
dependent.var1 capital_central 17.03438 11.380373 22.68838
dependent.var2 north_east 14.21727 10.843482 17.59106
dependent.var3 west 20.58123 18.698212 22.46425
dependent.var4 east 13.89538 10.497414 17.29334
dependent.var5 south 17.67935 14.975385 20.38332
dependent.var6 north 11.34279 7.427003 15.25858

0.31 Statistics

two sample ttest on difference in means (two sided): p=0.351; df=450

0.31.1 Number of Female New born ( < 2yr)

0.32 Graph

0.33 Table

region numbers lover conf. limit upper conf. limit
dependent.var south_east 1.1955399 0.7618014 1.6292784
dependent.var1 capital_central 0.3120975 0.0497760 0.5744191
dependent.var2 north_east 0.5281456 0.2519025 0.8043888
dependent.var3 west 0.4206420 0.2694100 0.5718740
dependent.var4 east 0.4413480 0.2397642 0.6429317
dependent.var5 south 0.5509862 0.3501815 0.7517908
dependent.var6 north 0.7182147 0.3288202 1.1076091

0.34 Statistics

two sample ttest on difference in means (two sided): p=0.444; df=450

0.34.1 If Number of Female New born “”( < 2yr) > 0“”, how many are physically disabled?

0.35 Graph

0.36 Table

region numbers lover conf. limit upper conf. limit
dependent.var north_east 0.0102298 -0.0121868 0.0326463
dependent.var1 south_east 0.0000000 0.0000000 0.0000000
dependent.var2 south 0.0324698 -0.0311698 0.0961095
dependent.var3 north 0.1911712 -0.0548270 0.4371694
dependent.var4 west 0.0000000 0.0000000 0.0000000
dependent.var5 east 0.0015534 -0.0017817 0.0048885
dependent.var6 capital_central 0.0106608 0.0106608 0.0106608

0.37 Statistics

two sample ttest on difference in means (two sided): p=0; df=156

0.37.1 If Number of Female New born ( < 2yr) > 0, how many have mental health concerns?

0.38 Graph

0.39 Table

region numbers lover conf. limit upper conf. limit
dependent.var north_east 0.0000000 0.0000000 0.0000000
dependent.var1 south_east 0.0045385 -0.0018263 0.0109033
dependent.var2 south 0.0000000 0.0000000 0.0000000
dependent.var3 north 0.0000000 0.0000000 0.0000000
dependent.var4 west 0.0000000 0.0000000 0.0000000
dependent.var5 east 0.0000000 0.0000000 0.0000000
dependent.var6 capital_central 0.0000000 0.0000000 0.0000000

0.40 Statistics

two sample ttest on difference in means (two sided): p=0.353; df=156

0.40.1 If Number of Female New born ( < 2yr) > 0, how many are chronically ill?

0.41 Graph

0.42 Table

region numbers lover conf. limit upper conf. limit
dependent.var north_east 0.0102298 -0.0121868 0.0326463
dependent.var1 south_east 0.0278207 -0.0034949 0.0591362
dependent.var2 south 0.0000000 0.0000000 0.0000000
dependent.var3 north 0.0077342 -0.0033309 0.0187994
dependent.var4 west 0.0000000 0.0000000 0.0000000
dependent.var5 east 0.0000000 0.0000000 0.0000000
dependent.var6 capital_central 0.0004523 -0.0005277 0.0014323

0.43 Statistics

two sample ttest on difference in means (two sided): p=0.34; df=156

0.43.1 Number of Male New born ( < 2yr)

0.44 Graph

0.45 Table

region numbers lover conf. limit upper conf. limit
dependent.var south_east 1.2072718 0.5978235 1.8167201
dependent.var1 capital_central 0.2305190 -0.0035799 0.4646179
dependent.var2 north_east 0.3243507 0.1024034 0.5462981
dependent.var3 west 0.2720817 0.1268115 0.4173519
dependent.var4 east 0.3718329 0.1991693 0.5444965
dependent.var5 south 0.6048204 0.3373487 0.8722920
dependent.var6 north 0.6152756 0.2785460 0.9520052

0.46 Statistics

two sample ttest on difference in means (two sided): p=0.342; df=450

0.46.1 If Number of Male New born ( < 2 yr) > 0, how many are physically disabled?

0.47 Graph

0.48 Table

region numbers lover conf. limit upper conf. limit
dependent.var north_east 0.0000000 0.0000000 0.0000000
dependent.var1 capital_central 0.0100063 -0.0116756 0.0316883
dependent.var2 south 0.0000000 0.0000000 0.0000000
dependent.var3 south_east 0.0000000 0.0000000 0.0000000
dependent.var4 east 0.0000000 0.0000000 0.0000000
dependent.var5 west 0.0000000 0.0000000 0.0000000
dependent.var6 north 0.3510706 -0.0069104 0.7090516

0.49 Statistics

two sample ttest on difference in means (two sided): p=0.351; df=128

0.49.1 If Number of Male New born ( < 2 yr) > 0, how many have mental health concerns?

0.50 Graph

0.51 Table

region numbers lover conf. limit upper conf. limit
dependent.var north_east 0.0000000 0.0000000 0.0000000
dependent.var1 capital_central 0.0002116 0.0002116 0.0002116
dependent.var2 south 0.0000000 0.0000000 0.0000000
dependent.var3 south_east 0.0518052 0.0264211 0.0771894
dependent.var4 east 0.0000000 0.0000000 0.0000000
dependent.var5 west 0.0000000 0.0000000 0.0000000
dependent.var6 north 0.0702141 -0.0784294 0.2188576

0.52 Statistics

two sample ttest on difference in means (two sided): p=0; df=128

0.52.1 If Number of Male New born ( < 2 yr ) > 0, how many are chronically ill?

0.53 Graph

0.54 Table

region numbers lover conf. limit upper conf. limit
dependent.var north_east 0.0000000 0.0000000 0.0000000
dependent.var1 capital_central 0.0000000 0.0000000 0.0000000
dependent.var2 south 0.0000000 0.0000000 0.0000000
dependent.var3 south_east 0.0061526 -0.0023922 0.0146974
dependent.var4 east 0.0000000 0.0000000 0.0000000
dependent.var5 west 0.0886989 -0.0851477 0.2625454
dependent.var6 north 0.0000000 0.0000000 0.0000000

0.55 Statistics

two sample ttest on difference in means (two sided): p=0.341; df=128

0.55.1 Number of young girls (2< 5yr)

0.56 Graph

0.57 Table

region numbers lover conf. limit upper conf. limit
dependent.var south_east 0.7320491 0.2837206 1.1803776
dependent.var1 capital_central 0.5406082 0.2581904 0.8230261
dependent.var2 north_east 0.5724631 0.2081867 0.9367395
dependent.var3 west 0.4101118 0.1798578 0.6403657
dependent.var4 east 0.6781030 0.3996715 0.9565344
dependent.var5 south 0.5705999 0.3229213 0.8182784
dependent.var6 north 1.0802127 0.6560990 1.5043264

0.58 Statistics

two sample ttest on difference in means (two sided): p=0.497; df=450

0.58.1 If >0, how many are physically disabled?

0.59 Graph

0.60 Table

region numbers lover conf. limit upper conf. limit
dependent.var capital_central 0.0007964 -0.0007646 0.0023575
dependent.var1 north_east 0.0000000 0.0000000 0.0000000
dependent.var2 east 0.0000000 0.0000000 0.0000000
dependent.var3 south 0.0000000 0.0000000 0.0000000
dependent.var4 south_east 0.0163084 -0.0027222 0.0353389
dependent.var5 west 0.0000000 0.0000000 0.0000000
dependent.var6 north 0.0526606 -0.0588220 0.1641432

0.61 Statistics

two sample ttest on difference in means (two sided): p=0.334; df=202

0.61.1 If >0, how many have mental health concerns?

0.62 Graph

0.63 Table

region numbers lover conf. limit upper conf. limit
dependent.var capital_central 0.0001286 -0.0001235 0.0003807
dependent.var1 north_east 0.0000000 0.0000000 0.0000000
dependent.var2 east 0.0000000 0.0000000 0.0000000
dependent.var3 south 0.0000000 0.0000000 0.0000000
dependent.var4 south_east 0.0000000 0.0000000 0.0000000
dependent.var5 west 0.0000000 0.0000000 0.0000000
dependent.var6 north 0.0000000 0.0000000 0.0000000

0.64 Statistics

two sample ttest on difference in means (two sided): p=0.334; df=202

0.64.1 If >0, how many are chronically ill?

0.65 Graph

0.66 Table

region numbers lover conf. limit upper conf. limit
dependent.var capital_central 0.1556642 -0.1494321 0.4607605
dependent.var1 north_east 0.0000000 0.0000000 0.0000000
dependent.var2 east 0.0812352 0.0812352 0.0812352
dependent.var3 south 0.0000000 0.0000000 0.0000000
dependent.var4 south_east 0.0000000 0.0000000 0.0000000
dependent.var5 west 0.0000000 0.0000000 0.0000000
dependent.var6 north 0.1053212 -0.0406438 0.2512862

0.67 Statistics

two sample ttest on difference in means (two sided): p=0.644; df=202

0.67.1 Number of young boys (2< 5yr)

0.68 Graph

0.69 Table

region numbers lover conf. limit upper conf. limit
dependent.var south_east 0.7188515 0.3238088 1.1138941
dependent.var1 capital_central 0.6248181 0.3507673 0.8988689
dependent.var2 north_east 0.6733177 0.3862567 0.9603787
dependent.var3 west 0.2836080 0.1351738 0.4320422
dependent.var4 east 0.6103959 0.4083857 0.8124060
dependent.var5 south 0.6773946 0.3941435 0.9606458
dependent.var6 north 1.0742247 0.7210646 1.4273849

0.70 Statistics

two sample ttest on difference in means (two sided): p=0.934; df=450

0.70.1 If >0, how many are physically disabled?

0.71 Graph

0.72 Table

region numbers lover conf. limit upper conf. limit
dependent.var capital_central 0.0002222 -0.0002133 0.0006576
dependent.var1 north_east 0.0000000 0.0000000 0.0000000
dependent.var2 south 0.0000000 0.0000000 0.0000000
dependent.var3 south_east 0.0018718 -0.0017968 0.0055403
dependent.var4 north 0.0000000 0.0000000 0.0000000
dependent.var5 east 0.0000000 0.0000000 0.0000000
dependent.var6 west 0.0000000 0.0000000 0.0000000

0.73 Statistics

two sample ttest on difference in means (two sided): p=0.318; df=204

0.73.1 If >0, how many have mental health concerns?

can not test group difference with <2 different values in the dependent variable